1 About

1.1 Contributions

Please note that authorship is alphabetical. Contributions are listed below - see github for details and who to blame for what :-).

1.3 Citation

If you wish to refer to any of the material from this report please cite as:

  • Anderson, B., (2019) Air Quality in Southampton (UK): Exploring the data , University of Southampton: Southampton, UK.

Report circulation:

  • Public

Report purpose:

This work is (c) 2019 the University of Southampton.

2 Introduction

# done in makefile

Data downloaded from http://southampton.my-air.uk. See also https://www.southampton.gov.uk/environmental-issues/pollution/air-quality/.

Southampton City Council collects various forms of air quality data at the sites shown in 2.1. WHO publishes information on the health consequences and “acceptable” exposure levels for each of these.

lDT <- data.table::melt(dt, id.vars = c("site", "obsDateTime"), measure.vars = c("co", "nox", "nox2", "noxes", "oz", "pm10", 
    "pm2_5", "so2"), value.name = "value"  # varies 
)

# remove NA
lDT <- lDT[!is.na(value)]

t <- lDT[, .(from = min(obsDateTime), to = max(obsDateTime), nObs = .N), keyby = .(site, variable)]

kableExtra::kable(t, caption = "Dates data available by site and measure", digits = 2) %>% kable_styling()
Table 2.1: Dates data available by site and measure
site variable from to nObs
Southampton - A33 Roadside AURN nox 2016-01-26 11:00:00 2019-12-04 22:00:00 33046
Southampton - A33 Roadside AURN nox2 2016-01-26 11:00:00 2019-12-04 22:00:00 33034
Southampton - A33 Roadside AURN pm10 2016-01-04 13:00:00 2019-12-04 21:00:00 31985
Southampton - Onslow Road nox 2016-01-01 00:00:00 2019-12-20 18:00:00 33509
Southampton - Onslow Road nox2 2016-01-01 00:00:00 2019-12-20 18:00:00 33505
Southampton - Onslow Road noxes 2016-01-01 00:00:00 2019-12-20 18:00:00 33509
Southampton - Victoria Road nox 2016-01-01 00:00:00 2019-12-20 18:00:00 25224
Southampton - Victoria Road nox2 2016-01-01 00:00:00 2019-12-20 18:00:00 25224
Southampton - Victoria Road noxes 2016-01-01 00:00:00 2019-12-20 18:00:00 25224
Southampton Background AURN nox 2016-01-01 00:00:00 2019-12-04 22:00:00 27948
Southampton Background AURN nox2 2016-01-01 00:00:00 2019-12-04 22:00:00 27961
Southampton Background AURN noxes 2016-01-01 00:00:00 2019-01-01 00:00:00 20026
Southampton Background AURN oz 2016-01-01 00:00:00 2019-12-04 22:00:00 27847
Southampton Background AURN pm10 2016-01-01 00:00:00 2019-12-04 22:00:00 24397
Southampton Background AURN pm2_5 2016-01-01 00:00:00 2019-12-04 22:00:00 26605
Southampton Background AURN so2 2016-01-01 00:00:00 2019-12-04 22:00:00 27399

3 Summarise data

Summarise previously downloaded and processed data… Note that this may not be completely up to date.

skimr::skim(dt)
Table 3.1: Data summary
Name dt
Number of rows 209832
Number of columns 10
_______________________
Column type frequency:
character 1
logical 1
numeric 7
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
site 0 1 22 31 0 6 0

Variable type: logical

skim_variable n_missing complete_rate mean count
co 209832 0 NaN :

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
nox 90105 0.57 26.61 40.23 -5.0 4.7 13.4 32.1 746.3 ▇▁▁▁▁
nox2 90108 0.57 37.00 22.54 -1.3 20.5 33.0 48.8 287.5 ▇▂▁▁▁
noxes 131073 0.38 76.59 74.87 -1.4 32.8 56.9 94.1 1431.8 ▇▁▁▁▁
pm10 153450 0.27 18.71 13.18 -3.5 10.9 15.8 23.1 761.6 ▇▁▁▁▁
oz 181985 0.13 40.50 23.12 -0.2 23.9 41.2 56.0 174.1 ▆▇▁▁▁
pm2_5 183227 0.13 12.15 9.58 -4.0 6.6 9.5 14.4 289.2 ▇▁▁▁▁
so2 182433 0.13 3.01 3.36 -1.1 0.9 1.7 3.7 50.5 ▇▁▁▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
obsDateTime 0 1 2016-01-01 2019-12-31 2017-12-30 12:30:00 34972 
t <- dt[, .(`co: Carbon Monoxide, mg/m3` = mean(co, na.rm = TRUE), `nox = Nitric Oxide, ug/m3` = mean(nox, na.rm = TRUE), 
    `nox2 = Nitrogen Dioxide, ug/m3` = mean(nox2, na.rm = TRUE), `noxes = Oxides of Nitrogen, ug/m3` = mean(noxes, na.rm = TRUE), 
    `oz = ozone, ug/m3` = mean(oz, na.rm = TRUE), `pm10, ug/m3` = mean(pm10, na.rm = TRUE), `pm2_5, ug/m3` = mean(pm2_5, 
        na.rm = TRUE), `so2 = Sulphur Dioxide, ug/m3` = mean(so2, na.rm = TRUE)), keyby = .(site)]

kableExtra::kable(t, caption = "Mean values per site (NaN indicates not measured)") %>% kable_styling()
Table 3.1: Mean values per site (NaN indicates not measured)
site co: Carbon Monoxide, mg/m3 nox = Nitric Oxide, ug/m3 nox2 = Nitrogen Dioxide, ug/m3 noxes = Oxides of Nitrogen, ug/m3 oz = ozone, ug/m3 pm10, ug/m3 pm2_5, ug/m3 so2 = Sulphur Dioxide, ug/m3
Southampton - A33 Roadside AURN NaN 34.00597 35.79520 NaN NaN 19.04556 NaN NaN
Southampton - Bitterne NaN NaN NaN NaN NaN NaN NaN NaN
Southampton - Onslow Road NaN 29.84889 42.20433 87.97470 NaN NaN NaN NaN
Southampton - Redbridge NaN NaN NaN NaN NaN NaN NaN NaN
Southampton - Victoria Road NaN 27.43040 39.90371 81.96375 NaN NaN NaN NaN
Southampton Background AURN NaN 13.24819 29.55140 50.78906 40.50492 18.27441 12.15377 3.014884

Table 3.1 gives an indication of the availability of the different measures.

4 Analysis

In this section we present graphical analysis of the previoulsy downloaded data. Note this is just a snapshot of the data available.

4.1 Nitrogen Dioxide

yLab <- "Nitrogen Dioxide (ug/m3)"

t <- lDT[variable == "nox2", .(mean = mean(value, na.rm = TRUE), sd = sd(value, na.rm = TRUE), min = min(value, na.rm = TRUE), 
    max = max(value, na.rm = TRUE)), keyby = .(site)]
kableExtra::kable(t, caption = "Summary of nox2 data") %>% kable_styling()
Table 4.1: Summary of nox2 data
site mean sd min max
Southampton - A33 Roadside AURN 35.79520 25.00895 0.0 164.3
Southampton - Onslow Road 42.20433 21.09972 1.2 241.3
Southampton - Victoria Road 39.90371 23.96440 -1.3 287.5
Southampton Background AURN 29.55140 16.94370 2.3 144.2

Table 4.1 suggests that there may be a few (5) negative values. These are summarised in 4.2 while Figure 4.1 shows the availability and levels of the pollutant data over time.

t <- head(dt[nox2 < 0], 10)
kableExtra::kable(t, caption = "Negative nox2 values (up to first 6)") %>% kable_styling()
Table 4.2: Negative nox2 values (up to first 6)
nox nox2 noxes pm10 site co oz pm2_5 so2 obsDateTime
0.4 -0.1 0.6 NA Southampton - Victoria Road NA NA NA NA 2016-01-29 04:00:00
0.8 -0.5 0.7 NA Southampton - Victoria Road NA NA NA NA 2016-11-27 05:00:00
0.9 -1.1 0.3 NA Southampton - Victoria Road NA NA NA NA 2016-11-27 06:00:00
1.5 -0.2 2.1 NA Southampton - Victoria Road NA NA NA NA 2017-02-06 00:00:00
1.1 -1.3 0.3 NA Southampton - Victoria Road NA NA NA NA 2017-02-06 01:00:00 
t <- table(dt[nox < 0]$site)
kableExtra::kable(t, caption = "Negative nox2 values (count by site)") %>% kable_styling()
Table 4.2: Negative nox2 values (count by site)
Var1 Freq
Southampton - Onslow Road 248
Southampton - Victoria Road 168 
# dt,xvar, yvar,fillVar, yLab
p <- makeTilePlot(lDT[variable == "nox2"], xVar = "obsDateTime", yVar = "site", fillVar = "value", yLab = yLab)

p
Nitrogen Dioxide data availability and levels over time

Figure 4.1: Nitrogen Dioxide data availability and levels over time 

# p <- ggplot2::ggplot(dt, aes(x = obsDateTime, y = nox2, colour = site, alpha = 0.1)) + geom_point(shape=4, size = 1)

t <- lDT[variable == "nox2" & value > 200][order(-value)]

kableExtra::kable(caption = paste0("Values greater than WHO threshold (NO2 > ", hourlyno2Threshold_WHO, ")"), head(t, 10)) %>% 
    kable_styling()
Table 4.3: Values greater than WHO threshold (NO2 > 200)
site obsDateTime variable value
Southampton - Victoria Road 2016-11-30 17:00:00 nox2 287.5
Southampton - Victoria Road 2016-11-30 19:00:00 nox2 275.3
Southampton - Victoria Road 2016-11-30 18:00:00 nox2 268.7
Southampton - Victoria Road 2017-01-05 18:00:00 nox2 251.6
Southampton - Victoria Road 2017-01-05 17:00:00 nox2 247.3
Southampton - Victoria Road 2017-01-24 18:00:00 nox2 243.5
Southampton - Victoria Road 2017-01-24 19:00:00 nox2 242.0
Southampton - Onslow Road 2016-03-08 09:00:00 nox2 241.3
Southampton - Victoria Road 2017-01-03 08:00:00 nox2 234.4
Southampton - Victoria Road 2017-01-03 09:00:00 nox2 232.8 
p <- makeDotPlot(lDT[variable == "nox2"], xVar = "obsDateTime", yVar = "value", byVar = "site", yLab = yLab)

p <- p + geom_hline(yintercept = hourlyno2Threshold_WHO) + labs(caption = "Reference line = WHO hourly guideline threshold")


if (doPlotly) {
    p
    plotly::ggplotly(p + xlim(xlimMinDateTime, xlimMaxDateTime))  # interactive, xlimited 
} else {
    p
}

Figure 4.2: Nitrogen Dioxide levels, Southampton (hourly)

Figure 4.2 shows hourly values for all sites. In the study period there were 23 hours when the hourly Nitrogen Dioxide level breached WHO guidelines. The worst 10 cases are shown in Table 4.3.

lDT[, obsDate := lubridate::date(obsDateTime)]

plotDT <- lDT[variable == "nox2", .(mean = mean(value, na.rm = TRUE)),
             keyby = .(obsDate, site)]

p <- makeDotPlot(plotDT, 
                 xVar = "obsDate", 
                 yVar = "mean", 
                 byVar = "site", 
                 yLab = yLab)

p <- p +
  geom_smooth() + # add smoothed line
  labs(caption = "Trend line = Generalized additive model (gam) with integrated smoothness estimation")

if(doPlotly){
  p
  plotly::ggplotly(p + xlim(xlimMinDate, xlimMaxDate)) # interactive, xlimited # interactive
} else {
  p
}

Figure 4.3: Nitrogen Dioxide levels, Southampton (daily mean

Figure 4.3 shows daily mean values for all sites over time and includes smoother trend lines for each site.

Clearly the mean daily values show less variance (and less extremes) than the hourly data and there has also been a decreasing trend over time.

4.2 PM 10

PM 10 data: has more sensors and wider coverage than PM2.5

yLab <- "PM 10 (ug/m3)"

t <- dt[, .(mean = mean(pm10, na.rm = TRUE), sd = sd(pm10, na.rm = TRUE), min = min(pm10, na.rm = TRUE), max = max(pm10, 
    na.rm = TRUE)), keyby = .(site)]
kableExtra::kable(t, caption = "Summary of pm10 data") %>% kable_styling()
Table 4.4: Summary of pm10 data
site mean sd min max
Southampton - A33 Roadside AURN 19.04556 14.36815 0.0 761.6
Southampton - Bitterne NaN NA Inf -Inf
Southampton - Onslow Road NaN NA Inf -Inf
Southampton - Redbridge NaN NA Inf -Inf
Southampton - Victoria Road NaN NA Inf -Inf
Southampton Background AURN 18.27441 11.42768 -3.5 344.1

Table 4.4 suggests that there may be a few (19) negative values. These are shown in 4.5 while 4.4 shows data availability and PM 10 levels over time at each site.

t <- head(dt[pm10 < 0], nrow(dt[pm10 < 0]))
kableExtra::kable(head(t), caption = "Negative PM10 values - first 6") %>% kable_styling()
Table 4.5: Negative PM10 values - first 6
nox nox2 noxes pm10 site co oz pm2_5 so2 obsDateTime
4.9 17.5 25.0 -0.9 Southampton Background AURN NA 48.7 -0.3 1.4 2016-04-16 10:00:00
3.1 15.1 19.8 -2.0 Southampton Background AURN NA 41.9 2.9 0.7 2016-06-12 06:00:00
5.7 15.3 24.0 -2.1 Southampton Background AURN NA 35.2 1.4 0.7 2016-06-12 07:00:00
1.0 8.6 10.1 -1.2 Southampton Background AURN NA 31.1 6.9 0.0 2017-06-11 03:00:00
5.0 13.4 21.0 -0.1 Southampton Background AURN NA 56.2 5.9 1.5 2017-06-30 11:00:00
1.1 11.2 12.9 -0.6 Southampton Background AURN NA 24.0 5.4 0.2 2017-07-04 02:00:00 
p <- makeTilePlot(lDT[variable == "pm10"], xVar = "obsDateTime", yVar = "site", fillVar = "value", yLab = yLab)

p
Availability and level of PM 10 data over time

Figure 4.4: Availability and level of PM 10 data over time

Figure 4.5 shows hourly PM 10 values for all sites and suggests there may be an extreme outlier (see Table 4.5).

t <- lDT[variable == "pm10" & value > 100][order(-value)]

kableExtra::kable(caption = "10 highest hourly values (PM 10 > 100)", head(t)) %>% kable_styling()
Table 4.6: 10 highest hourly values (PM 10 > 100)
site obsDateTime variable value obsDate
Southampton - A33 Roadside AURN 2016-09-19 14:00:00 pm10 761.6 2016-09-19
Southampton - A33 Roadside AURN 2016-08-09 14:00:00 pm10 402.7 2016-08-09
Southampton Background AURN 2016-01-01 00:00:00 pm10 344.1 2016-01-01
Southampton - A33 Roadside AURN 2017-05-05 01:00:00 pm10 336.4 2017-05-05
Southampton - A33 Roadside AURN 2016-08-09 15:00:00 pm10 328.0 2016-08-09
Southampton - A33 Roadside AURN 2016-08-09 04:00:00 pm10 300.1 2016-08-09 
p <- makeDotPlot(lDT[variable == "pm10"], xVar = "obsDateTime", yVar = "value", byVar = "site", yLab = yLab)


if (doPlotly) {
    p
    plotly::ggplotly(p + xlim(xlimMinDateTime, xlimMaxDateTime))  # interactive, xlimited # interactive
} else {
    p
}

Figure 4.5: PM10 levels, Southampton (hourly) 

pm10DT <- dt[!is.na(pm10)]
plotDT <- lDT[variable == "pm10", .(mean = mean(value, na.rm = TRUE)),
             keyby = .(obsDate, site)]

extremePm10Daily <- plotDT[mean > dailyPm10Threshold_WHO][order(-mean)]

kableExtra::kable(caption = paste0("10 highest values greater than WHO threshold (PM 10 > ", 
                                   dailyPm10Threshold_WHO , ")"), 
                  digits = 2,
                  head(extremePm10Daily, 10)) %>%
  kable_styling()
Table 4.7: 10 highest values greater than WHO threshold (PM 10 > 50)
obsDate site mean
2016-08-09 Southampton - A33 Roadside AURN 144.48
2017-06-19 Southampton - A33 Roadside AURN 143.66
2017-05-05 Southampton - A33 Roadside AURN 88.94
2016-09-19 Southampton - A33 Roadside AURN 88.46
2016-11-30 Southampton - A33 Roadside AURN 79.25
2016-03-12 Southampton - A33 Roadside AURN 72.00
2017-01-22 Southampton - A33 Roadside AURN 66.86
2019-02-27 Southampton Background AURN 64.67
2019-02-27 Southampton - A33 Roadside AURN 60.60
2019-04-17 Southampton Background AURN 58.37 
p <- makeDotPlot(plotDT, 
                 xVar = "obsDate", 
                 yVar = "mean", 
                 byVar = "site", 
                 yLab = yLab)

p <- p + 
  geom_hline(yintercept = dailyPm10Threshold_WHO) +
  geom_smooth() + # add smoothed line
  labs(caption = "Trend line = Generalized additive model (gam) with integrated smoothness estimation")


if(doPlotly){
  p
  plotly::ggplotly(p + xlim(xlimMinDate, xlimMaxDate)) # interactive, xlimited # interactive
} else {
  p
}

Figure 4.6: PM10 levels, Southampton (daily mean, WHO daily threshold shown in red - use mouse to hover over data)

Figure 4.6 shows daily values for all sites and indicates the 78 that cross the WHO PM10 daily mean exposure threshold (50) - see Table 4.7.

4.3 PM 2.5

yLab <- "PM 2.5 (ug/m3)"

t <- dt[, .(mean = mean(pm2_5, na.rm = TRUE), sd = sd(pm2_5, na.rm = TRUE), min = min(pm2_5, na.rm = TRUE), max = max(pm2_5, 
    na.rm = TRUE)), keyby = .(site)]
kableExtra::kable(t, caption = "Summary of pm2_5 data") %>% kable_styling()
Table 4.8: Summary of pm2_5 data
site mean sd min max
Southampton - A33 Roadside AURN NaN NA Inf -Inf
Southampton - Bitterne NaN NA Inf -Inf
Southampton - Onslow Road NaN NA Inf -Inf
Southampton - Redbridge NaN NA Inf -Inf
Southampton - Victoria Road NaN NA Inf -Inf
Southampton Background AURN 12.15377 9.584743 -4 289.2

Table 4.8 suggests that there may be a few (89) negative values. These are shown in 4.9 while 4.7 shows data availability and PM 2.5 levels over time at each site.

t <- head(dt[pm2_5 < 0], nrow(dt[pm2_5 < 0]))
kableExtra::kable(head(t), caption = "Negative pm2_5 values - first 6") %>% kable_styling()
Table 4.9: Negative pm2_5 values - first 6
nox nox2 noxes pm10 site co oz pm2_5 so2 obsDateTime
41.8 55.2 119.2 5.8 Southampton Background AURN NA 16.8 -0.8 4.5 2016-01-02 17:00:00
2.5 26.4 30.1 12.6 Southampton Background AURN NA 48.1 -0.9 0.2 2016-01-04 03:00:00
2.9 24.2 28.6 11.7 Southampton Background AURN NA 44.0 -0.3 0.8 2016-01-04 23:00:00
1.0 15.7 17.3 9.1 Southampton Background AURN NA 49.8 -1.0 1.1 2016-01-05 01:00:00
2.5 14.5 18.3 7.8 Southampton Background AURN NA 69.5 -0.3 4.6 2016-01-09 22:00:00
3.5 18.2 23.6 5.6 Southampton Background AURN NA 60.8 -0.9 4.9 2016-01-09 23:00:00 
p <- makeTilePlot(lDT[variable == "pm2_5"], xVar = "obsDateTime", yVar = "site", fillVar = "value", yLab = yLab)

p
Availability and level of PM 10 data over time

Figure 4.7: Availability and level of PM 10 data over time

Figure 4.8 shows hourly values for all sites.

t <- lDT[variable == "pm2_5" & value > 200][order(-value)]

kableExtra::kable(caption = "Extreme hourly values (PM 2.5 > 50)", t) %>% kable_styling()
Table 4.10: Extreme hourly values (PM 2.5 > 50)
site obsDateTime variable value obsDate
Southampton Background AURN 2016-01-01 00:00:00 pm2_5 289.2 2016-01-01
Southampton Background AURN 2019-10-27 20:00:00 pm2_5 239.1 2019-10-27 
p <- makeDotPlot(lDT[variable == "pm2_5"], xVar = "obsDateTime", yVar = "value", byVar = "site", yLab = yLab)


if (doPlotly) {
    p
    plotly::ggplotly(p + xlim(xlimMinDateTime, xlimMaxDateTime))  # interactive, xlimited # interactive
} else {
    p
}

Figure 4.8: PM2_5 levels, Southampton (hourly - use mouse to hover over data) 

plotDT <- lDT[variable == "pm2_5", .(mean = mean(value, na.rm = TRUE)),
             keyby = .(obsDate, site)]

extremePm25Daily <- plotDT[mean > dailyPm2.5Threshold_WHO][order(-mean)]

kableExtra::kable(caption = paste0("10 highest values greater than WHO threshold (PM 2.5 > ", 
                                   dailyPm2.5Threshold_WHO , ")"), 
                  digits = 2,
                  head(extremePm25Daily,10)) %>%
  kable_styling()
Table 4.11: 10 highest values greater than WHO threshold (PM 2.5 > 25)
obsDate site mean
2016-03-12 Southampton Background AURN 64.79
2019-04-17 Southampton Background AURN 49.04
2016-03-13 Southampton Background AURN 47.79
2019-04-07 Southampton Background AURN 46.90
2019-02-27 Southampton Background AURN 43.64
2019-03-29 Southampton Background AURN 43.60
2019-04-22 Southampton Background AURN 42.92
2017-02-12 Southampton Background AURN 42.25
2018-04-21 Southampton Background AURN 41.96
2016-03-11 Southampton Background AURN 41.80 
p <- makeDotPlot(plotDT, 
                 xVar = "obsDate", 
                 yVar = "mean", 
                 byVar = "site", 
                 yLab = yLab)

p <- p + 
  geom_hline(yintercept = dailyPm2.5Threshold_WHO) +
  geom_smooth() + #add smoothed line
  labs(caption = "Trend line = Generalized additive model (gam) with integrated smoothness estimation")


if(doPlotly){
  p
  plotly::ggplotly(p + xlim(xlimMinDate, xlimMaxDate)) # interactive, xlimited # interactive
} else {
  p
}

Figure 4.9: PM2_5 levels, Southampton (daily mean)

Figure 4.9 shows daily values for all sites and indicates that the WHO PM2_5 daily mean exposure threshold (25) was breached on 2 days. The 10 worst cases are shown in Table 4.11.

5 Observations

  • Nitorgen Dioxide levels appear to be trending downwards
  • For particulates the trend is more complex with PM10 trending down but PM2.5 trending up.
    • Something happened on the 27th October 2019 at 20:00. There are spikes on all hourly plots (although this is masked in the daily plots). Could it have been a cruise ship leaving?
    • Something else happened on the 2nd December 2019 at 21:00. Was this another ship?

6 Annex

6.1 Nitrogen Dioxide

t <- lDT[variable == "nox2"]

skim(t)
Table 6.1: Data summary
Name t
Number of rows 119724
Number of columns 5
_______________________
Column type frequency:
character 1
Date 1
factor 1
numeric 1
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
site 0 1 25 31 0 4 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
obsDate 0 1 2016-01-01 2019-12-20 2018-01-16 1447

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
variable 0 1 FALSE 1 nox: 119724, co: 0, nox: 0, nox: 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
value 0 1 37 22.54 -1.3 20.5 33 48.8 287.5 ▇▂▁▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
obsDateTime 0 1 2016-01-01 2019-12-20 18:00:00 2018-01-16 15:30:00 34723 
t <- lDT[variable == "nox2" & value > hourlyno2Threshold_WHO][order(-value)]

kableExtra::kable(caption = paste0("Values greater than WHO threshold (NO2 > ", hourlyno2Threshold_WHO, ")"), t) %>% kable_styling()
Table 6.2: Values greater than WHO threshold (NO2 > 200)
site obsDateTime variable value obsDate
Southampton - Victoria Road 2016-11-30 17:00:00 nox2 287.5 2016-11-30
Southampton - Victoria Road 2016-11-30 19:00:00 nox2 275.3 2016-11-30
Southampton - Victoria Road 2016-11-30 18:00:00 nox2 268.7 2016-11-30
Southampton - Victoria Road 2017-01-05 18:00:00 nox2 251.6 2017-01-05
Southampton - Victoria Road 2017-01-05 17:00:00 nox2 247.3 2017-01-05
Southampton - Victoria Road 2017-01-24 18:00:00 nox2 243.5 2017-01-24
Southampton - Victoria Road 2017-01-24 19:00:00 nox2 242.0 2017-01-24
Southampton - Onslow Road 2016-03-08 09:00:00 nox2 241.3 2016-03-08
Southampton - Victoria Road 2017-01-03 08:00:00 nox2 234.4 2017-01-03
Southampton - Victoria Road 2017-01-03 09:00:00 nox2 232.8 2017-01-03
Southampton - Victoria Road 2017-01-05 19:00:00 nox2 226.6 2017-01-05
Southampton - Victoria Road 2016-11-30 16:00:00 nox2 222.6 2016-11-30
Southampton - Victoria Road 2016-12-29 09:00:00 nox2 218.7 2016-12-29
Southampton - Onslow Road 2016-03-08 08:00:00 nox2 216.0 2016-03-08
Southampton - Victoria Road 2017-01-05 20:00:00 nox2 214.3 2017-01-05
Southampton - Victoria Road 2017-01-24 20:00:00 nox2 212.7 2017-01-24
Southampton - Victoria Road 2016-11-30 09:00:00 nox2 209.9 2016-11-30
Southampton - Onslow Road 2016-03-12 22:00:00 nox2 208.5 2016-03-12
Southampton - Onslow Road 2016-03-08 07:00:00 nox2 208.1 2016-03-08
Southampton - Victoria Road 2016-11-30 20:00:00 nox2 207.0 2016-11-30
Southampton - Onslow Road 2016-02-19 08:00:00 nox2 206.3 2016-02-19
Southampton - Victoria Road 2016-01-19 19:00:00 nox2 204.4 2016-01-19
Southampton - Onslow Road 2016-03-21 08:00:00 nox2 202.8 2016-03-21

6.2 PM 10

t <- lDT[variable == "pm10"]

skim(t)
Table 6.3: Data summary
Name t
Number of rows 56382
Number of columns 5
_______________________
Column type frequency:
character 1
Date 1
factor 1
numeric 1
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
site 0 1 27 31 0 2 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
obsDate 0 1 2016-01-01 2019-12-04 2018-02-12 1410

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
variable 0 1 FALSE 1 pm1: 56382, co: 0, nox: 0, nox: 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
value 0 1 18.71 13.18 -3.5 10.9 15.8 23.1 761.6 ▇▁▁▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
obsDateTime 0 1 2016-01-01 2019-12-04 22:00:00 2018-02-12 23:00:00 33494 
kableExtra::kable(caption = paste0("PM 10 values greater than WHO threshold (NO2 > ", hourlyno2Threshold_WHO, ")"), extremePm10Daily) %>% 
    kable_styling()
Table 6.4: PM 10 values greater than WHO threshold (NO2 > 200)
obsDate site mean
2016-08-09 Southampton - A33 Roadside AURN 144.48125
2017-06-19 Southampton - A33 Roadside AURN 143.66000
2017-05-05 Southampton - A33 Roadside AURN 88.94348
2016-09-19 Southampton - A33 Roadside AURN 88.45625
2016-11-30 Southampton - A33 Roadside AURN 79.25417
2016-03-12 Southampton - A33 Roadside AURN 72.00000
2017-01-22 Southampton - A33 Roadside AURN 66.86250
2019-02-27 Southampton Background AURN 64.67143
2019-02-27 Southampton - A33 Roadside AURN 60.59524
2019-04-17 Southampton Background AURN 58.37083
2016-12-01 Southampton - A33 Roadside AURN 54.80833
2017-01-26 Southampton - A33 Roadside AURN 53.90000
2019-03-29 Southampton Background AURN 53.69167
2017-12-19 Southampton Background AURN 53.66250
2016-12-06 Southampton - A33 Roadside AURN 53.45000
2019-04-17 Southampton - A33 Roadside AURN 53.08750
2019-04-07 Southampton Background AURN 52.94167
2018-04-21 Southampton Background AURN 52.32083
2019-03-30 Southampton Background AURN 51.66250
2019-04-22 Southampton Background AURN 50.86250
2019-02-23 Southampton Background AURN 50.21667

6.3 PM 2.5

t <- lDT[variable == "pm2_5"]

skim(t)
Table 6.5: Data summary
Name t
Number of rows 26605
Number of columns 5
_______________________
Column type frequency:
character 1
Date 1
factor 1
numeric 1
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
site 0 1 27 27 0 1 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
obsDate 0 1 2016-01-01 2019-12-04 2018-04-20 1131

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
variable 0 1 FALSE 1 pm2: 26605, co: 0, nox: 0, nox: 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
value 0 1 12.15 9.58 -4 6.6 9.5 14.4 289.2 ▇▁▁▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
obsDateTime 0 1 2016-01-01 2019-12-04 22:00:00 2018-04-20 15:00:00 26605 
kableExtra::kable(caption = paste0("PM 2.5 values greater than WHO threshold (NO2 > ", hourlyno2Threshold_WHO, ")"), extremePm25Daily) %>% 
    kable_styling()
Table 6.6: PM 2.5 values greater than WHO threshold (NO2 > 200)
obsDate site mean
2016-03-12 Southampton Background AURN 64.79167
2019-04-17 Southampton Background AURN 49.04167
2016-03-13 Southampton Background AURN 47.79167
2019-04-07 Southampton Background AURN 46.90417
2019-02-27 Southampton Background AURN 43.63810
2019-03-29 Southampton Background AURN 43.59583
2019-04-22 Southampton Background AURN 42.92500
2017-02-12 Southampton Background AURN 42.24583
2018-04-21 Southampton Background AURN 41.96250
2016-03-11 Southampton Background AURN 41.80000
2019-04-16 Southampton Background AURN 41.77500
2019-03-30 Southampton Background AURN 40.80000
2017-12-19 Southampton Background AURN 40.45833
2016-01-20 Southampton Background AURN 39.66667
2017-09-26 Southampton Background AURN 39.12083
2017-03-27 Southampton Background AURN 38.15833
2019-04-08 Southampton Background AURN 37.65833
2019-04-18 Southampton Background AURN 37.00417
2019-02-15 Southampton Background AURN 36.48571
2019-02-23 Southampton Background AURN 35.80833
2018-05-08 Southampton Background AURN 35.52917
2018-05-07 Southampton Background AURN 35.44583
2017-02-13 Southampton Background AURN 35.42083
2018-05-06 Southampton Background AURN 34.07917
2016-03-17 Southampton Background AURN 33.73333
2018-04-11 Southampton Background AURN 33.45000
2019-01-24 Southampton Background AURN 33.33750
2018-04-14 Southampton Background AURN 33.20417
2018-12-27 Southampton Background AURN 33.03333
2018-11-22 Southampton Background AURN 32.90833
2017-02-11 Southampton Background AURN 32.72917
2017-04-08 Southampton Background AURN 32.65833
2019-12-02 Southampton Background AURN 32.61667
2016-03-23 Southampton Background AURN 32.31667
2018-11-23 Southampton Background AURN 32.30417
2019-12-03 Southampton Background AURN 32.15417
2018-05-26 Southampton Background AURN 32.12083
2017-11-03 Southampton Background AURN 31.53333
2019-04-15 Southampton Background AURN 31.52917
2017-04-09 Southampton Background AURN 31.25417
2019-04-23 Southampton Background AURN 31.11250
2016-01-01 Southampton Background AURN 30.73750
2017-03-24 Southampton Background AURN 30.70833
2019-02-24 Southampton Background AURN 30.55417
2018-11-24 Southampton Background AURN 30.48333
2017-09-27 Southampton Background AURN 30.29583
2018-04-12 Southampton Background AURN 30.21667
2017-11-02 Southampton Background AURN 30.10833
2019-03-31 Southampton Background AURN 29.72500
2019-10-27 Southampton Background AURN 29.64167
2018-10-05 Southampton Background AURN 29.54167
2019-02-25 Southampton Background AURN 29.40000
2018-03-25 Southampton Background AURN 29.12083
2019-04-21 Southampton Background AURN 28.97083
2017-02-14 Southampton Background AURN 28.96250
2019-03-28 Southampton Background AURN 28.46250
2019-04-19 Southampton Background AURN 28.36250
2016-01-19 Southampton Background AURN 28.12917
2017-02-10 Southampton Background AURN 27.43333
2017-08-28 Southampton Background AURN 27.42500
2019-01-04 Southampton Background AURN 27.05000
2018-11-05 Southampton Background AURN 26.94167
2019-02-26 Southampton Background AURN 26.87391
2018-01-12 Southampton Background AURN 26.85417
2018-12-28 Southampton Background AURN 26.82500
2016-05-07 Southampton Background AURN 26.77500
2019-01-21 Southampton Background AURN 26.65000
2019-04-20 Southampton Background AURN 26.57917
2017-06-21 Southampton Background AURN 26.56667
2017-03-28 Southampton Background AURN 26.47500
2019-04-06 Southampton Background AURN 26.32917
2018-05-20 Southampton Background AURN 26.03333
2018-05-24 Southampton Background AURN 25.93750
2017-11-01 Southampton Background AURN 25.77500
2016-05-12 Southampton Background AURN 25.75000
2018-04-19 Southampton Background AURN 25.75000
2017-06-20 Southampton Background AURN 25.74167
2018-05-29 Southampton Background AURN 25.70833
2019-12-04 Southampton Background AURN 25.51304
2017-05-06 Southampton Background AURN 25.04167

7 Runtime

Report generated using knitr in RStudio with R version 3.6.2 (2019-12-12) running on x86_64-apple-darwin15.6.0 (Darwin Kernel Version 17.7.0: Sun Dec 1 19:19:56 PST 2019; root:xnu-4570.71.63~1/RELEASE_X86_64).

t <- proc.time() - startTime

elapsed <- t[[3]]

Analysis completed in 29.904 seconds ( 0.5 minutes).

R packages used:

  • data.table - (Dowle et al. 2015)
  • ggplot2 - (Wickham 2009)
  • here - (Müller 2017)
  • kableExtra - (Zhu 2018)
  • lubridate - (Grolemund and Wickham 2011)
  • plotly - (Sievert et al. 2016)
  • skimr - (Arino de la Rubia et al. 2017)

References

Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.

Müller, Kirill. 2017. Here: A Simpler Way to Find Your Files. https://CRAN.R-project.org/package=here.

Sievert, Carson, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec, and Pedro Despouy. 2016. Plotly: Create Interactive Web Graphics via ’Plotly.js’. https://CRAN.R-project.org/package=plotly.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Zhu, Hao. 2018. KableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.